Reinforcement learning is a type of learning at the cutting edge of current research into neural networks and machine learning. Unlike unsupervised and supervised learning, reinforcement learning makes decisions based upon the results of an action. It is a goal-oriented learning process, similar to that used by many parents and teachers across the world. We teach children to study and perform well on tests so that they receive high grades as a reward. Likewise, reinforcement learning can be used to teach machines to make choices that will result in the highest reward.
There are four main components to reinforcement learning: the actor or agent, the state or scenario, the chosen action, and the reward. The actor is the object or vehicle making the decisions within the application. The state is the world the actor exists within. Any decision the actor makes occurs within the parameters of the state. The action is simply the choice the actor makes when given a set of options...